Abstract

Ontology forms a key emerging domain that has a vast potential for improving the organizing, managing and understanding of information. It plays a vital role in facilitating the access of content, communications, interoperations and in the provision of qualitative and novel services on Semantic Web transformation.

The discipline of machine learning (ML) facilitates computers to aid humans in analyzing vast complex repositories of data. The present paper reviews extant literature from the past decade related to the use of machine learning methods in the context of ontology engineering.

Research Overview

10+ Years

Comprehensive literature review period

Multiple

ML approaches analyzed

4+ Domains

Industry applications studied

Research Objective

Certain key ML approaches are identified in this study with general guidelines on the practical uses of ML in varied domains including banking, healthcare, agriculture, and food industries.

Key Topics Covered

Ontology Fundamentals

Core concepts, structures, and principles of ontology engineering

ML Techniques

Classification, clustering, and neural network approaches

Semantic Web

Role of ontologies in semantic web transformation

Domain Applications

Practical implementations across multiple industries

Research Significance

Comprehensive review of ML methods applied to ontology engineering over the past decade
Identification of key approaches and best practices for practical implementation
Guidelines for applying ML techniques in domain-specific ontology development
Framework for future research and development in automated ontology engineering

Machine Learning Methods in Ontology Engineering: A Literature Review